Cheryl Metzger / February 1, 2017

Part II: Data and Business in 2017—“Building a Better Analytics Strategy”

The message is clear: In 2017, all businesses must think about their data strategy. But as vital new information deluges business leaders, are they ready to navigate uncharted waters? Are they leveraging data in the right way to take meaningful action? Or are leaders getting lost in the numbers?

In this second of my three-part series on Data Analytics in 2017, I turned to my panel of data analytics experts and enthusiasts for some perspective and tips on how to build a smarter approach to data.

The data adoption curve

“The good thing is we’ve got all this data and the bad thing is we’ve got all this data.”

That’s the sentiment expressed in many companies when faced with the data deluge. It’s something James Barbee, an engineer at an energy utility, heard as his company began to collect data from newly installed smart meters. As with other companies, the data transformation in his company began in the IT department. From there it began to spread organically, as James describes:

“We were inundated with all this new data, so when we were addressing customer queries, we’d start looking to dive into the data [for answers]. I started asking our IT guys about getting reports, and learned they were querying a database… so I just kind of reverse engineered SQL and dove in.”

As engineers like James became more adept at gathering insights from the investment in new data sources, upper management began to embrace it as well. “Now,” he shared, "groups in the company that used to be ambivalent about data are looking for it.”

Not all adoption curves are so smooth, however, with many organizations facing knowledge barriers that limit widespread adoption of analytics. Association of National Advertiser’s Kiran Goojha, who has worked in data analytics across a number of industries, has seen the challenges of this first-hand: “Over the last couple of years, there’s been an increasing need to educate not just the C-suite but everyone at the company on the importance of utilizing data to inform the decisions [to enable teams to ask] the right questions…and to make sure that analytics report doesn’t just go into a drawer.” Wire Stone's Jon Baker has seen the barriers to adoption at executive levels: “As you get further up the executive chain it gets less sophisticated. I don’t see business leaders looking for more data.”

The data sophistication curve

For other businesses on the opposite side of the spectrum, data is widely embraced and leveraged. But executives at data-driven organizations still face a number of challenges. For one, too much data. According to the Harvard Business Review, “Vast amounts of data mean managers struggle to prioritize what’s important. In the end, they end up applying arbitrary data toward new problems, reaching a subpar solution.”

The result is that data scientists and business leaders are often forced to oversimplify. Siegel + Gale’s Brian Rafferty has seen decision makers ask for a single silver-bullet metric—an aggregate of many data points—in an effort to create a streamlined way to vet and validate decisions. But, Brian notes, such metrics often fail because they become “impenetrable” making it “completely impossible to unpack the amalgam of what it is. Ultimately it doesn’t help you to know what to do.”

Knowing what to do with the insights is only part of the challenge, however. Taking action on insight is another, and that requires close collaboration with the technology side. After all, Rosetta’s Ed Falconer notes, “If you don’t have the ability to execute on the insights, why are you doing the analytics?... Execution and activation are critical—you need to have the right technology solution to enable the analytics team.” This pressing need to have the technology in place to deliver on analytics insights may partially explain why chief digital officer roles have been doubling year on year over the last decade. One thing is clear: For leaders of data-driven organizations, the pressure is on. In a marketplace rich with customer data, cautions Jon, “If you don’t understand your customer very well you will get exposed eventually.”

Building a better strategy

So what should business and marketing leaders do? Whether your business is only beginning down the data adoption path or is deepening its sophistication, my panel of data experts recommend the following tips for building a successful data analytics strategy:

Ease into data adoptionJames recommends taking a few steps to encourage data adoption throughout the enterprise: First, give people clarity about the analytics process, what inputs were used, and how insights were calculated. Communicate that data is just one of many tools for problem-solving, not competing with, but complementing, established processes. Build models that can be refined and optimized over time; always look to test and learn. And don’t take humans out of the equation, they will help ensure models represent the real world and real human behavior.

Take your data inventoryJon recommends taking stock of your current data sources and capabilities. “Get a complete view of what data you have available today and what kind of signals you might be able to get off that data. Then look at what other companies and services are supporting that business—what data do they have that could help complete the picture? Third-party data can often determine whether the business is successful or not. It often has the signals that determine important implications for the business.”

Once your owned data inventory is complete, VML’s Mark Donatelli recommends taking a look at public or free sources of data, often overlooked by analysts, that can nevertheless impact your business.

Fill gapsBeyond data, Mark recommends taking inventory of your analytics tools and skills and comparing those to what you need to achieve your objective. From there, leaders can make a plan to “buy, build, or partner to fill the gaps.”

Get specific for faster insightsEd recommends leaders narrow the focus of their inquiries: “Focus on something specific and build from it, instead of trying to capture every data point possible. Orienting your attention to a smaller sub-problem means you will be able to show something back to your business as an actionable outcome immediately…If you don’t go into it with that kind of mindset, you overwhelm yourself and your corporation.”

Build a roadmap to examine complex questionsHere, too, Ed recommends breaking a problem down into smaller parts. “Ask, ‘what problem am I trying to solve? Where am I trying to get to? And then what stepping stones and sub-problems do I think I can solve in order to get there?’” By breaking the problem into smaller component parts, the data analyst can start to determine what analytical steps will be needed to reach the ultimate destination. This is especially useful when trying to understand a process. He recommends plotting the key decisions in that process and then identifying the corresponding data points reflecting those decisions. This will help build what Ed calls a data decision journey, a tool that will help build a more finely tuned analytic strategy.

Don’t just hire data analysts, hire data storytellersMaking analytics actionable requires that it be more than merely digestible—it must also tell a persuasive story. Kiran highlights that this special skill set is critical to achieving executive buy-in.

Visualize your dataHand in hand with data storytelling is great data visualization. “Just seeing the data for yourself is [often] enough to change behaviors,” says Mark. Sometimes with the right visualization, he suggests, no further storytelling would be necessary.

Use data to craft thought leadershipBrian notes that data is not only about making decisions. It’s also an invaluable source of intellectual property that brands can leverage to craft insightful thought leadership. This is especially true, he notes, in manufacturing and industrial sectors, which typically are instrumented with an “Internet of Things”— sensors and systems automated to collect real-time cloud-based data. By leveraging business data to identify trends and patterns, brands can serve up “information that’s interesting to customers while simultaneously showcasing what they can do.”

If there’s anything my panel of data analytics professionals agreed upon, it is the need for further investment in analytics, not just at a financial level but at a cultural and practical level. Educating teams on the value and application of analytics across the business will ultimately yield greater value to customers and internal stakeholders. With the right methodologies and tools, the business can unlock new competitive advantage and opportunities for thought leadership. And with the right skill sets and people, businesses can go from describing the past to innovating for the future.

Continue on to Part III to explore what personalities, skill sets, and resources make for the best data analytics hires in 2017.